Magnetic resonance imaging (MRI) is a medical imaging modality that can create images of the inside of a human body without using x-rays or other ionizing radiation. MRI uses a powerful magnet to create a strong, uniform, static magnetic field (i.e., the “main magnetic field”). When a human body, or part of a human body, is placed in the main magnetic field, the nuclear spins that are associated with the hydrogen nuclei in tissue water become polarized. This means that the magnetic moments that are associated with these spins become preferentially aligned along the direction of the main magnetic field, resulting in a small net tissue magnetization along that axis (the “z axis,” by convention). A MRI system also comprises components called gradient coils that produce smaller amplitude, spatially varying magnetic fields when current is applied to them. Typically, gradient coils are designed to produce a magnetic field component along the z axis and that varies linearly in amplitude with position along one of the x, y or z axes. The effect of a gradient coil is to create a small ramp on the magnetic field strength, and concomitantly on the resonance frequency of the nuclear spins, along a single axis. Three gradient coils with orthogonal axes are used to “spatially encode” the MR signal by creating a signature resonance frequency at each location in the body. Radio frequency (RF) coils are used to create pulses of RF energy at or near the resonance frequency of the hydrogen nuclei. These coils are used to add energy to the nuclear spin system in a controlled fashion. As the nuclear spins then relax back to their rest energy state, they give up energy in the form of an RF signal. This signal is detected by the MRI system and is transformed into an image using a computer and known reconstruction algorithms.
One technique that has been developed to accelerate MR data acquisition is commonly referred to as “parallel imaging” or “partially parallel imaging.” In parallel imaging, multiple receive coils acquire data from a region or volume of interest. Thus, parallel imaging is used to accelerate data acquisition in one or more dimensions by exploiting the spatial dependence of phased array coil sensitivity. Parallel imaging has been shown to be successful in reducing scan time, image blurring and geometric distortions. Moreover, parallel imaging can be used to improve spatial or temporal resolution as well as increased volumetric coverage.
There are several types of parallel imaging (PI) reconstruction methods that have been developed to generate the final, unaliased image from accelerated data. These methods can generally be divided into two categories based on how they treat the reconstruction problem: 1) SENSE-based techniques (Sensitivity Encoding) estimate coil sensitivity profiles from low-resolution calibration images, which can then be used to unwrap aliased pixels in image space using a direct inversion algorithm; and 2) autocalibrating PI-based methods, such as GRAPPA (Generalized Auto-calibrating Partially Parallel Acquisition) and ARC (Autocalibrating Reconstruction for Cartesian sampling), that calculate reconstruction weights necessary to synthesize unacquired data from acquired data using an algorithm that does not require coil sensitivity estimates. The reconstruction weights for GRAPPA and ARC are calculated from a small amount of fully sampled calibration data that is typically embedded within the scan (“auto-calibration”), but can also be acquired before or after the scan. Thus, GRAPPA and ARC exploit receiver coil sensitivity variation to accelerate data acquisition and synthesize the missing data using pre-calculated calibration information obtained from the particular imaging setup. While both SENSE- and autocalibrating PI-based approaches have been successful, in practice, autocalibrating PI-based techniques have been shown to be preferred when accurate coil sensitivity estimates cannot be obtained, for example, in reduced FOV applications, and because they exhibit relatively benign image artifacts across a variety of applications.
Per-coil reconstructions can be used with autocalibrating PI techniques to eliminate the phase-cancellation artifacts that can result from poor coil combination. However, a disadvantage of per-coil autocalibrating PI techniques is that they are computationally expensive because a complete dataset for each receiver coil is reconstructed from the accelerated data from that coil and the accelerated data from at least one other coil. The linear combination weights needed to perform the reconstruction are calculated during an initial training phase. The training phase is performed using a small amount of fully sampled calibration data that is acquired on each receiver coil either before, during, or after the accelerated scan. Reconstruction weights can be determined by fitting the calibration data from all coils to the calibration data on a single coil in the group. This process can be repeated for each coil in the group.
In other words, for various autocalibrating PI techniques, each individual coil dataset is reconstructed using information from a plurality of coils. The multiple separate coil images can then be combined via a sum-of-squares reconstruction to create a final composite image. While performing a per-coil reconstruction eliminates the phase cancellation problems observed in implementations such as VD-AUTO-SMASH, it introduces a significant computation burden. Specifically, the computational expense of autocalibrating PI techniques is proportional to Nc2, where Nc is the number of surface coils. Thus, computation time scales exponentially as the number of coils increases.
It would be desirable to provide a system and method for sampling and reconstruction of an image using parallel imaging that improves computational efficiency and maintains good image quality.